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REVIEW 3 major objections 4 minor 1 cited by

More than One Step at a Time: Designing Procedural Feedback for Non-visual Makeup Routines

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper's central claim is that assistive makeup technology should provide procedural, context-aware feedback guided by an eleven-category taxonomy of user questions.

desk verdict A genuinely useful taxonomy for non-visual makeup feedback, but the paper's claim that it is grounded in 'actual language, not hypothetical scenarios' outruns the methods as written; fixable with a provenance audit. read the letter →

arxiv 2507.03942 v1 pith:HI5SOQJY submitted 2025-07-05 cs.HC cs.CV

Franklin Mingzhe Li , Akihiko Oharazawa , Chloe Qingyu Zhu , Misty Fan , Daisuke Sato , Chieko Asakawa , Patrick Carrington This is my paper
classification cs.HCcs.CV
keywords non-visualmakeupproceduralfeedbackcontextualinquiryvisualimpairmentaccessibilitytaxonomyassistivetechnologyconversationalinterfaces
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the missing piece in assistive makeup technology is procedural feedback: systems that follow the user through the multi-step routine and answer the questions that actually arise in the moment. It grounds this in a contextual inquiry with fifteen visually impaired makeup users and interviews with five professional makeup artists. The central deliverable is a taxonomy of eleven recurring feedback-need categories, from step confirmation and color to symmetry, blending, and mistake detection. If the taxonomy is right, future assistive systems should be hands-free, conversational, and context-aware, supporting the whole procedure rather than only discrete tasks like color identification. A sympathetic reader would care because this reframes accessible beauty as supporting users' embodied expertise and agency instead of compensating for a missing sense.

What carries the argument

The central object is the taxonomy of questions and feedback needs presented in Table 3, built from the questions participants asked during real-time makeup application. The taxonomy carries the argument by turning scattered observations into a checklist of answerable query types an assistive system would need to handle, from 'Is one side darker than the other?' to 'What product needs fixing?' The supporting mechanism is the two-phase study design: a contextual inquiry with think-aloud application, which surfaces the questions in context, and professional makeup-artist video review, which validates and extends the procedural guidance. The taxonomy's eleven categories are the load-bearing structure for the paper's design implications.

What would settle it

A diary or survey study with a broader sample, including people who have abandoned makeup, men and nonbinary users, and users from non-Western beauty cultures, that finds a frequent, recurring question type falling outside the eleven taxonomy categories would show the taxonomy is incomplete. A simpler check is to ask a new set of visually impaired users to apply makeup and tag their spoken questions against the taxonomy; if a substantial share of questions cannot be tagged, the framework misses real needs.

Watch

Extended reading notes

Core claim

The authors discovered that visually impaired makeup users have already built rich tactile-first, procedural strategies, such as mirrored side-by-side learning, finger-based application, and memorized step orders, but that existing tools leave them without reliable real-time assessment. Across the routine, participants asked questions that fall into eleven categories: step confirmation, color and shade, placement and location, symmetry and alignment, quantity and amount, blending and integration, instruction and walkthroughs, technique and strategy, tools and product recommendations, mistake detection and hazards, and adjustment and overall assessment. The authors claim that this taxonomy, grounded in participants' own words, captures what an assistive system must be able to answer to support non-visual makeup, and that professional makeup artists' reflections affirm the tactile-first strategies while pointing to product knowledge and error-preventing sequencing as underleveraged. The paper's central claim is that moving from discrete assistance to procedural, context-aware feedback is the right design direction.

Load-bearing premise

The paper assumes that the fifteen female, makeup-experienced, mostly North American participants it observed represent the range of questions and strategies that visually impaired makeup users in general would have.

Editorial extensions

If this is right

  • Assistive makeup systems should provide feedback during the routine, not only after it, so errors can be caught before they compound.
  • Feedback must be hands-free and conversational, since users' hands and attention are occupied by the application itself.
  • The taxonomy gives a concrete checklist for design: a system that cannot answer questions about step confirmation, color, placement, symmetry, quantity, blending, instruction, technique, tools, mistakes, and adjustment is incomplete.
  • Success metrics for non-visual makeup should be user-defined and contextual rather than visual ideals such as perfect symmetry.
  • Tactile-first techniques like finger application and mirrored learning should be scaffolded by assistive systems, not replaced.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the taxonomy generalizes, the same procedural-feedback framing could apply to other visually guided embodied routines, such as hairstyling, skincare, or dressing, where users also need step-level confirmation and mistake detection.
  • The taxonomy could be turned into a testable system specification: a conversational assistant that maps each user question to one of the eleven categories and a sensing modality, whether camera, touch, or self-report, that can answer it; studies could then measure whether covering all eleven categories improves completion and confidence.
  • Because the sample was limited to women already engaged with makeup, a direct next study would recruit people who stopped wearing makeup because of accessibility barriers, as well as men and nonbinary users, to check whether the taxonomy gains or loses categories.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper presents a two-phase qualitative study of non-visual makeup routines. In the first phase, the authors conducted a contextual inquiry with 15 visually impaired makeup users, combining live makeup application with think-aloud verbalization, pre- and post-application reflections, and follow-up discussion. In the second phase, five licensed professional makeup artists reviewed participant videos and responded to participant-raised questions. The paper reports procedural behaviors (goal-driven routines, learning pathways, step-level adaptations), assessment practices, and a taxonomy of eleven categories of questions and feedback needs (Table 3), along with frequency counts (Figure 5). It concludes with design implications for hands-free, conversational, context-aware assistive systems for blind and low-vision makeup users.

Significance. If the findings hold, the paper fills a clear gap: prior assistive technology for makeup has focused on isolated tasks like color identification or product labeling, while the procedural, step-by-step information demands of non-visual makeup have been understudied. The study's strengths include the use of real application sessions rather than interviews alone, the dual perspective of users and professional artists, concrete illustrative quotes, and a limitation section that candidly acknowledges the all-female, experienced, North America-based sample. The taxonomy in Table 3 is a practical contribution that could directly inform the design of conversational and context-aware assistive systems. However, as detailed below, the evidentiary grounding of the taxonomy is not fully specified, and the frequency-based claims in Figure 5 are not yet reproducible from the manuscript.

major comments (3)
  1. [§5.3 vs. §3.1.2] The claim in §5.3 that the taxonomy is 'grounded in participants’ actual language and lived experience, not hypothetical scenarios' is not supported by the reported protocol. Section 3.1.2 shows that the Follow-Up Discussion explicitly asked participants what feedback they 'would seek' from a professional makeup artist and how assistive technology 'could support' their process, both of which are hypothetical or reflective prompts. No analysis step is reported that separates in-situ think-aloud questions from these prompted retrospective questions. As a result, the taxonomy and Figure 5's frequency counts appear to mix two evidentiary sources with different statuses, and the claim that these are the 'real recurring questions' an assistive system must answer is overstated as written. The authors should either add a provenance audit showing how each coded instance was sourced, or qualify the claim to distinguish observed in-routine questions from anticipated or desired support discussed in reflection.
  2. [§3.3, Table 3, Figure 5] The paper does not report a coding scheme, a coding-unit definition, or inter-rater reliability for the taxonomy. Section 3.3 describes thematic analysis at a high level but does not state how verbalizations were segmented into individual 'questions,' how the eleven categories in Table 3 were derived and defined, or how the frequency counts in Figure 5 were tallied (e.g., per participant, per utterance, per unique question). This makes the central quantitative claim of the paper—the relative importance of different feedback needs—non-reproducible. The authors should provide the codebook, describe the unitization and coding procedure, and ideally report reliability statistics (e.g., Cohen's kappa) for at least a subset of the data.
  3. [Table 3] Several taxonomy categories appear to overlap in ways that are not resolved by the descriptions or examples. For instance, 'Placement and Location,' 'Symmetry and Alignment,' and 'Blending and Integration' could all apply to a single utterance such as 'Is this blush in the right place, even on both sides, and blended?' The table gives no mutually exclusive definitions or coding rules, so it is unclear how ambiguous instances were assigned. Without such rules, the frequency distribution in Figure 5 may be an artifact of coder judgment rather than a stable property of the data. The authors should either define mutually exclusive categories with explicit decision rules, or report that multiple codes per utterance were allowed and adjust the frequency analysis accordingly.
minor comments (4)
  1. [Table 1] In the Makeup Frequency column, 'Once a couple week' (P11) is a typo for 'Once a couple of weeks.'
  2. [Figure 2] The caption states 'The number inside each cell indicates the order of the makeup application,' but it is not clear whether this order is the modal sequence, an average, or a representative sequence from one participant. The figure legend should explain how ordering was aggregated across the 15 participants.
  3. [§5.1.1] The quote 'I prefer to go to the same agent in the Chanel counter' appears to be missing 'at' before 'the Chanel counter'; please verify the transcription.
  4. [§6.6.5] The statement 'This relational framing aligns with prior work on cooperative AI and human-centered automation' has no citation; adding one or two supporting references would strengthen the argument.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular dependency: the taxonomy is an empirical qualitative summary of participant data; self-citations are background and not load-bearing.

full rationale

The paper's central contribution, the taxonomy of feedback needs (Table 3) and its frequencies (Figure 5), is produced by thematic analysis of contextual-inquiry transcripts (Section 3.3), not by fitting a parameter, instantiating a prior model, or importing a conclusion from the authors' earlier work. No equation or construction makes the output equivalent to an input: the categories are labels for participant questions ('Am I completing this step?', 'Is one side darker than the other?'), and the paper does not claim to predict these from any prior quantity. The multiple self-citations (e.g., [1], [38], [51]) appear as background literature, motivation, or alignment support; for instance, Section 2.2 cites Li et al. only to note that earlier participants used video calls, which motivates the study but does not entail the taxonomy. Section 6.1's appeal to prior work on tactile expertise is confirmatory framing, not the evidentiary basis of the findings. The Section 5.3 statement that the taxonomy is 'grounded in participants' actual language and lived experience, not hypothetical scenarios' sits in some tension with the Section 3.1.2 protocol, which explicitly solicited hypothetical or retrospective feedback ('what feedback they would seek from a professional makeup artist'), and the paper does not report a provenance audit separating prompted from spontaneous utterances. This is a validity and transparency concern about the strength of the empirical claim, not a circularity: even if some instances were prompted, the taxonomy would still be an inductive summary of interview responses rather than a derivation that reduces to its own inputs. Section 7 also candidly limits generalizability by sample composition. No circular step can be exhibited, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on standard qualitative assumptions about self-report validity, thematic analysis as an organizing method, and expert authority. No free parameters or invented entities are introduced; the taxonomy is an analytic construct derived from participant questions.

assumptions (3)
  • domain assumption Participants' think-aloud and interview responses accurately reflect their makeup practices and needs.
    The study relies on self-report and verbalization during a live application; this is a standard qualitative assumption.
  • domain assumption Thematic analysis of 15 transcripts is sufficient to identify recurring patterns for a taxonomy.
    Sample size and coding choices are not formally justified, but they are consistent with qualitative HCI norms.
  • domain assumption Five professional makeup artists provide valid complementary perspectives on non-visual technique.
    Expert reviews are used to affirm and augment participant strategies; their authority is assumed without independent validation.

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Cite this review

Pith. "Pith review of More than One Step at a Time: Designing Procedural Feedback for Non-visual Makeup Routines." pith.science (2026). https://pith.science/paper/HI5SOQJY

@misc{pith2026250703942,
  author       = {Pith},
  title        = {Pith review of: More than One Step at a Time: Designing Procedural Feedback for Non-visual Makeup Routines},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HI5SOQJY}},
  note         = {Machine review of arXiv:2507.03942}
}
read the original abstract

Makeup plays a vital role in self-expression, identity, and confidence - yet remains an underexplored domain for assistive technology, especially for people with vision impairments. While existing tools support isolated tasks such as color identification or product labeling, they rarely address the procedural complexity of makeup routines: coordinating step sequences, managing product placement, and assessing the final look with accessible feedback. To understand the real-world process, we conducted a contextual inquiry with 15 visually impaired makeup users, capturing real-time makeup application behaviors and their step-by-step information needs and assessment approaches. Our findings reveal embodied, tactile-first strategies; persistent challenges in blending, symmetry, and assessment; and a desire for honest, real-time, goal-aligned feedback. We also interviewed five professional makeup artists, who reviewed participant makeup videos and provided expert responses to participant-raised questions and assessment practices. We contribute a taxonomy of feedback needs in non-visual makeup, and outline design implications for future assistive systems - emphasizing hands-free, conversational interaction and context-aware, procedural support for expressive and independent beauty practices.

Figures

Figures reproduced from arXiv: 2507.03942 by the authors.

Figure 1
Figure 1. Demonstration of mirrored, side-by-side learning approach. The left figure shows a makeup artist apply makeup for the blind [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Makeup procedures for non-visual makeup among 15 participants. The number inside each cell indicates the order of the [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. P7 is using EL-VMA app [33] from her phone to obtain feedback on eye shadow. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Categories of questions and feedback needs during non-visual makeup routines, as defined in our taxonomy (see Table 3). This [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Frequency of each question type asked by participants during non-visual makeup routines. This figure emphasizes which [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multi-modal Information Between Reality and Videos

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    AROMA pairs a blind cook's spoken descriptions of what they feel, smell, and taste with a wearable camera and a video recipe to answer questions and issue proactive alerts, and eight participants rated it usable despi...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.